Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	34.0	3.778037
daal4py_KNeighborsClassifier	0.0	2.0	23.643444
KNeighborsClassifier_kd_tree	0.0	2.0	32.052489
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	26.916706
KMeans_tall	0.0	0.0	22.365274
daal4py_KMeans_tall	0.0	0.0	8.406904
KMeans_short	0.0	0.0	2.710550
daal4py_KMeans_short	0.0	0.0	1.364891
LogisticRegression	0.0	0.0	19.761589
daal4py_LogisticRegression	0.0	0.0	4.238672
Ridge	0.0	0.0	11.438993
daal4py_Ridge	0.0	0.0	1.998984
HistGradientBoostingClassifier	0.0	5.0	8.080488
lightgbm	0.0	5.0	25.801415
xgboost	0.0	5.0	42.813769
catboost	0.0	5.0	4.885658
total	1.0	2.0	0.341927

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.284	0.000	2.818	0.000	1	100	NaN	NaN	0.492	0.000	0.576	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.278	0.238	0.000	0.024	1	100	0.928	0.938	1.751	0.009	13.864	0.152	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.212	0.001	0.000	0.212	1	100	1.000	1.000	0.090	0.001	2.354	0.030	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.137	0.000	5.826	0.000	1	1	NaN	NaN	0.483	0.000	0.284	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.697	0.058	0.000	0.014	1	1	0.698	0.825	1.689	0.008	8.111	0.050	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.201	0.001	0.000	0.201	1	1	1.000	0.000	0.090	0.002	2.236	0.040	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.139	0.000	5.768	0.000	1	5	NaN	NaN	0.482	0.000	0.288	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.876	0.054	0.000	0.024	1	5	0.782	0.704	1.689	0.009	14.134	0.081	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.212	0.001	0.000	0.212	1	5	1.000	0.000	0.093	0.009	2.274	0.210	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.139	0.000	5.755	0.000	-1	1	NaN	NaN	0.483	0.000	0.288	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.175	0.149	0.000	0.025	-1	1	0.698	0.825	1.686	0.005	14.928	0.100	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.175	0.013	0.000	0.175	-1	1	1.000	0.000	0.090	0.001	1.939	0.146	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.141	0.000	5.678	0.000	-1	5	NaN	NaN	0.484	0.000	0.291	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.457	0.000	0.000	0.035	-1	5	0.782	0.938	1.748	0.008	20.284	0.091	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.179	0.014	0.000	0.179	-1	5	1.000	1.000	0.090	0.001	1.998	0.157	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.132	0.000	6.070	0.000	-1	100	NaN	NaN	0.482	0.000	0.273	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.530	0.000	0.000	0.036	-1	100	0.928	0.704	1.694	0.016	20.975	0.200	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.187	0.011	0.000	0.187	-1	100	1.000	0.000	0.094	0.010	1.991	0.240	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.297	0.000	1	100	NaN	NaN	0.100	0.000	0.537	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.234	0.019	0.000	0.019	1	100	0.980	0.970	0.301	0.002	63.917	0.388	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.026	0.001	0.000	0.026	1	100	1.000	1.000	0.005	0.001	4.982	0.567	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.305	0.000	1	1	NaN	NaN	0.100	0.000	0.523	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.720	0.006	0.000	0.011	1	1	0.975	0.969	0.254	0.000	42.174	0.048	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.013	0.001	0.000	0.013	1	1	1.000	1.000	0.005	0.001	2.636	0.347	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.305	0.000	1	5	NaN	NaN	0.100	0.000	0.524	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.205	0.012	0.000	0.019	1	5	0.979	0.960	0.254	0.002	75.549	0.658	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.026	0.001	0.000	0.026	1	5	1.000	1.000	0.005	0.000	5.083	0.511	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.304	0.000	-1	1	NaN	NaN	0.101	0.000	0.522	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	22.114	0.095	0.000	0.022	-1	1	0.975	0.969	0.255	0.001	86.799	0.425	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.003	0.000	0.020	-1	1	1.000	1.000	0.005	0.000	4.015	0.704	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.301	0.000	-1	5	NaN	NaN	0.100	0.000	0.530	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.618	0.000	0.000	0.031	-1	5	0.979	0.970	0.301	0.002	101.623	0.685	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.032	0.001	0.000	0.032	-1	5	1.000	1.000	0.005	0.001	6.091	0.654	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.052	0.000	0.305	0.000	-1	100	NaN	NaN	0.101	0.000	0.520	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	30.574	0.000	0.000	0.031	-1	100	0.980	0.960	0.253	0.000	120.855	0.178	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.034	0.003	0.000	0.034	-1	100	1.000	1.000	0.005	0.000	6.611	0.820	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.732	0.000	0.029	0.000	1	100	NaN	NaN	0.681	0.000	4.012	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.635	0.016	0.000	0.005	1	100	0.977	0.961	0.099	0.001	46.969	0.406	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	1	100	1.000	1.000	0.000	0.000	12.340	6.581	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.740	0.000	0.029	0.000	-1	100	NaN	NaN	0.668	0.000	4.102	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.657	0.008	0.000	0.003	-1	100	0.977	0.977	0.181	0.003	14.705	0.244	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.000	0.000	14.318	6.766	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.775	0.000	0.029	0.000	-1	1	NaN	NaN	0.670	0.000	4.145	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.425	0.003	0.000	0.000	-1	1	0.956	0.961	0.104	0.012	4.102	0.478	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	8.520	4.158	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.748	0.000	0.029	0.000	-1	5	NaN	NaN	0.673	0.000	4.086	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.794	0.004	0.000	0.001	-1	5	0.980	0.977	0.180	0.002	4.403	0.055	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	7.256	3.266	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.752	0.000	0.029	0.000	1	1	NaN	NaN	0.665	0.000	4.141	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.710	0.003	0.000	0.001	1	1	0.956	0.972	0.536	0.002	1.324	0.008	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.001	0.000	1.321	0.637	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.743	0.000	0.029	0.000	1	5	NaN	NaN	0.666	0.000	4.120	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.364	0.009	0.000	0.001	1	5	0.980	0.972	0.536	0.004	2.547	0.024	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.001	0.000	1.592	0.748	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.729	0.000	0.022	0.000	1	100	NaN	NaN	0.431	0.000	1.691	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.045	0.003	0.000	0.000	1	100	0.979	0.968	0.001	0.000	60.241	19.136	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	6.332	4.599	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.728	0.000	0.022	0.000	-1	100	NaN	NaN	0.435	0.000	1.673	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.041	0.002	0.000	0.000	-1	100	0.979	0.982	0.001	0.000	36.393	10.505	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	20.134	15.371	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.733	0.000	0.022	0.000	-1	1	NaN	NaN	0.428	0.000	1.713	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.002	0.001	0.000	-1	1	0.966	0.968	0.001	0.000	33.746	10.677	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	20.246	15.970	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.727	0.000	0.022	0.000	-1	5	NaN	NaN	0.427	0.000	1.702	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.025	0.001	0.001	0.000	-1	5	0.977	0.982	0.001	0.000	24.446	6.523	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	20.706	16.259	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.727	0.000	0.022	0.000	1	1	NaN	NaN	0.430	0.000	1.690	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.022	0.001	0.001	0.000	1	1	0.966	0.983	0.006	0.001	3.549	0.470	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.342	4.063	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.727	0.000	0.022	0.000	1	5	NaN	NaN	0.432	0.000	1.683	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.001	0.001	0.000	1	5	0.977	0.983	0.006	0.001	3.809	0.565	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.984	3.558	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.573	0.0	0.837	0.000	random	NaN	30	NaN	0.371	0.0	1.545	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.376	0.000	random	0.001	30	0.001	0.000	0.0	9.194	6.356	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.510	7.136	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.550	0.0	0.873	0.000	k-means++	NaN	30	NaN	0.404	0.0	1.360	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.385	0.000	k-means++	0.001	30	0.000	0.000	0.0	8.914	5.774	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.717	8.031	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.252	0.0	3.839	0.000	random	NaN	30	NaN	2.890	0.0	2.164	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.727	0.000	random	0.002	30	0.002	0.000	0.0	5.974	2.640	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	random	1.000	30	1.000	0.000	0.0	10.234	6.915	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.242	0.0	3.845	0.000	k-means++	NaN	30	NaN	3.064	0.0	2.037	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.631	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.786	2.883	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.337	7.200	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.072	0.000	0.044	0.000	random	NaN	20	NaN	0.091	0.0	0.791	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.193	0.000	random	0.001	20	0.001	0.001	0.0	2.661	0.436	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.113	5.543	See	See
3	KMeans_short	fit	10000	10000	2	20	0.217	0.000	0.015	0.000	k-means++	NaN	20	NaN	0.028	0.0	7.673	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.000	0.192	0.000	k-means++	0.000	20	0.002	0.001	0.0	2.657	0.576	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.000	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.814	6.710	See	See
6	KMeans_short	fit	10000	10000	100	20	0.188	0.000	0.851	0.000	random	NaN	20	NaN	0.309	0.0	0.608	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.001	6.185	0.000	random	0.301	20	0.301	0.001	0.0	2.366	0.636	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.012	0.001	random	1.000	20	1.000	0.000	0.0	7.550	3.604	See	See
9	KMeans_short	fit	10000	10000	100	20	0.576	0.000	0.278	0.000	k-means++	NaN	20	NaN	0.124	0.0	4.654	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.000	6.745	0.000	k-means++	0.300	20	0.325	0.001	0.0	2.172	0.386	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.000	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	8.120	4.537	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	10.950	0.0	[-0.10774664]	0.000	NaN	NaN	NaN	NaN	NaN	1.998	0.0	5.481	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[52.69703421]	0.000	NaN	NaN	NaN	NaN	0.56	0.000	0.0	0.887	0.456	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.2552628]	0.000	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.345	0.324	See	See
3	LogisticRegression	fit	1000	1000	10000	[27]	0.808	0.0	[-2.64338962]	0.001	NaN	NaN	NaN	NaN	NaN	0.830	0.0	0.974	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[27]	0.002	0.0	[134.92220048]	0.000	NaN	NaN	NaN	NaN	0.29	0.003	0.0	0.552	0.127	See	See
5	LogisticRegression	predict	1000	1	10000	[27]	0.000	0.0	[26.03645587]	0.000	NaN	NaN	NaN	NaN	0.00	0.001	0.0	0.118	0.091	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.179	0.000	0.447	0.0	NaN	NaN	NaN	0.184	0.000	0.975	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.011	0.001	6.974	0.0	NaN	NaN	0.109	0.019	0.001	0.606	0.037	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	1.250	0.0	NaN	NaN	NaN	0.000	0.000	0.642	0.654	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.433	0.000	0.558	0.0	NaN	NaN	NaN	0.247	0.000	5.796	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	5.609	0.0	NaN	NaN	1.000	0.000	0.000	0.623	0.434	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.015	0.0	NaN	NaN	NaN	0.000	0.000	0.607	0.687	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}